Efficient Top-k Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling

We propose an indexing scheme for top-k shortest-path distance queries on graphs, which is useful in a wide range of important applications such as network-aware searches and link prediction. While many efficient methods for answering standard (top-1) distance queries have been developed, none of these methods are directly extensible to top-k distance queries. We develop a new framework for top-k distance queries based on 2-hop cover and then present an efficient indexing algorithm based on the recently proposed pruned landmark labeling scheme. The scalability, efficiency and robustness of our method is demonstrated in extensive experimental results. Moreover, we demonstrate the usefulness of top-k distance queries by applying them to link prediction, the most fundamental graph problem in the AI and Web communities.